


from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel

@require_tf
class     A__   :







    """simple docstring"""

    __magic_name__         =						BlenderbotSmallConfig
    __magic_name__         =						{}
    __magic_name__         =						'gelu'
    def __init__(			self     ,   __snake_case     ,   __snake_case=1_3     ,   __snake_case=7     ,   __snake_case=True     ,   __snake_case=False     ,   __snake_case=9_9     ,   __snake_case=3_2     ,   __snake_case=2     ,   __snake_case=4     ,   __snake_case=3_7     ,   __snake_case=0.1     ,   __snake_case=0.1     ,   __snake_case=2_0     ,   __snake_case=2     ,   __snake_case=1     ,   __snake_case=0     ,   ):
         snake_case											=							parent
         snake_case											=							batch_size
         snake_case											=							seq_length
         snake_case											=							is_training
         snake_case											=							use_labels
         snake_case											=							vocab_size
         snake_case											=							hidden_size
         snake_case											=							num_hidden_layers
         snake_case											=							num_attention_heads
         snake_case											=							intermediate_size

         snake_case											=							hidden_dropout_prob
         snake_case											=							attention_probs_dropout_prob
         snake_case											=							max_position_embeddings
         snake_case											=							eos_token_id
         snake_case											=							pad_token_id
         snake_case											=							bos_token_id
    def 						a_     (			self       ):
         snake_case											=							ids_tensor([self.batch_size, self.seq_length - 1]     ,   self.vocab_size       )
         snake_case											=							tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       )     ,   1       )
         snake_case											=							tf.concat([input_ids, eos_tensor]     ,   axis=1       )

         snake_case											=							ids_tensor([self.batch_size, self.seq_length]     ,   self.vocab_size       )

         snake_case											=							self.config_cls(
             vocab_size=self.vocab_size     ,   d_model=self.hidden_size     ,   encoder_layers=self.num_hidden_layers     ,   decoder_layers=self.num_hidden_layers     ,   encoder_attention_heads=self.num_attention_heads     ,   decoder_attention_heads=self.num_attention_heads     ,   encoder_ffn_dim=self.intermediate_size     ,   decoder_ffn_dim=self.intermediate_size     ,   dropout=self.hidden_dropout_prob     ,   attention_dropout=self.attention_probs_dropout_prob     ,   max_position_embeddings=self.max_position_embeddings     ,   eos_token_ids=[2]     ,   bos_token_id=self.bos_token_id     ,   pad_token_id=self.pad_token_id     ,   decoder_start_token_id=self.pad_token_id     ,   **self.config_updates     ,   )
         snake_case											=							prepare_blenderbot_small_inputs_dict(__snake_case     ,   __snake_case     ,   __snake_case       )
         return config, inputs_dict




    def 						a_     (			self     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							TFBlenderbotSmallModel(config=__snake_case       ).get_decoder()
         snake_case											=							inputs_dict['''input_ids''']

         snake_case											=							input_ids[:1, :]
         snake_case											=							inputs_dict['''attention_mask'''][:1, :]
         snake_case											=							inputs_dict['''head_mask''']
         snake_case											=							1

         # first forward pass
         snake_case											=							model(__snake_case     ,   attention_mask=__snake_case     ,   head_mask=__snake_case     ,   use_cache=__snake_case       )

         snake_case					,    snake_case											=							outputs.to_tuple()

         # create hypothetical next token and extent to next_input_ids
         snake_case											=							ids_tensor((self.batch_size, 3)     ,   config.vocab_size       )
         snake_case											=							tf.cast(ids_tensor((self.batch_size, 3)     ,   2       )     ,   tf.inta       )

         # append to next input_ids and
         snake_case											=							tf.concat([input_ids, next_tokens]     ,   axis=-1       )
         snake_case											=							tf.concat([attention_mask, next_attn_mask]     ,   axis=-1       )

         snake_case											=							model(__snake_case     ,   attention_mask=__snake_case       )[0]
         snake_case											=							model(__snake_case     ,   attention_mask=__snake_case     ,   past_key_values=__snake_case       )[0]

         self.parent.assertEqual(next_tokens.shape[1]     ,   output_from_past.shape[1]       )

         # select random slice
         snake_case											=							int(ids_tensor((1,)     ,   output_from_past.shape[-1]       )       )
         snake_case											=							output_from_no_past[:, -3:, random_slice_idx]
         snake_case											=							output_from_past[:, :, random_slice_idx]

         # test that outputs are equal for slice
         tf.debugging.assert_near(__snake_case     ,   __snake_case     ,   rtol=1E-3       )






def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_=None				,UpperCamelCase_=None				,UpperCamelCase_=None				,UpperCamelCase_=None				,UpperCamelCase_=None				,):



     """simple docstring"""






     if attention_mask is None:
          snake_case											=							tf.cast(tf.math.not_equal(UpperCamelCase_				,config.pad_token_id							)				,tf.inta							)
     if decoder_attention_mask is None:
          snake_case											=							tf.concat(
              [
                  tf.ones(decoder_input_ids[:, :1].shape				,dtype=tf.inta							),
                  tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]				,config.pad_token_id							)				,tf.inta							),
              ]				,axis=-1				,)
     if head_mask is None:
          snake_case											=							tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
     if decoder_head_mask is None:
          snake_case											=							tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
     if cross_attn_head_mask is None:
          snake_case											=							tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
     return {
         "input_ids": input_ids,
         "decoder_input_ids": decoder_input_ids,
         "attention_mask": attention_mask,
         "decoder_attention_mask": decoder_attention_mask,
         "head_mask": head_mask,
         "decoder_head_mask": decoder_head_mask,
         "cross_attn_head_mask": cross_attn_head_mask,
     }

@require_tf
class     A__   (       snake_case__			,   snake_case__			,   unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						(
        (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
    )
    __magic_name__         =						(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
    __magic_name__         =						(
        {
            'conversational': TFBlenderbotSmallForConditionalGeneration,
            'feature-extraction': TFBlenderbotSmallModel,
            'summarization': TFBlenderbotSmallForConditionalGeneration,
            'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
            'translation': TFBlenderbotSmallForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    __magic_name__         =						True
    __magic_name__         =						False
    __magic_name__         =						False
    def 						a_     (			self       ):
         snake_case											=							TFBlenderbotSmallModelTester(self       )
         snake_case											=							ConfigTester(self     ,   config_class=__snake_case       )
    def 						a_     (			self       ):
         self.config_tester.run_common_tests()




    def 						a_     (			self       ):
         snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*__snake_case       )






@require_tokenizers
@require_tf
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						[
        'Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '
        '  i\'m going to throw up.\nand why is that?'
    ]
    __magic_name__         =						'facebook/blenderbot_small-90M'
    @cached_property
    def 						a_     (			self       ):
         # use "old" tokenizer here because of bug when downloading new tokenizer
         return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''       )
    @cached_property
    def 						a_     (			self       ):
         snake_case											=							TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
         return model




    @slow
    def 						a_     (			self       ):
         snake_case											=							self.tokenizer(self.src_text     ,   return_tensors='''tf'''       )
         snake_case											=							self.model.generate(
             model_inputs.input_ids     ,   attention_mask=model_inputs.attention_mask     ,   num_beams=2     ,   use_cache=__snake_case     ,   )
         snake_case											=							self.tokenizer.batch_decode(generated_ids.numpy()     ,   skip_special_tokens=__snake_case       )[0]
         assert generated_words in (
             "i don't know. i just feel like i'm going to throw up. it's not fun.",
             "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
             "i'm not sure. i just feel like i've been in a bad situation.",
         )








import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list

@require_torchaudio
@require_sentencepiece
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def 						a_     (			self       ):
         snake_case											=							'''laion/clap-htsat-unfused'''
         snake_case											=							tempfile.mkdtemp()
    def 						a_     (			self     ,   **__snake_case       ):
         return RobertaTokenizer.from_pretrained(self.checkpoint     ,   **__snake_case       )
    def 						a_     (			self     ,   **__snake_case       ):
         return ClapFeatureExtractor.from_pretrained(self.checkpoint     ,   **__snake_case       )
    def 						a_     (			self       ):
         shutil.rmtree(self.tmpdirname       )
    def 						a_     (			self       ):
         snake_case											=							self.get_tokenizer()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							ClapProcessor(tokenizer=__snake_case     ,   feature_extractor=__snake_case       )

         processor.save_pretrained(self.tmpdirname       )
         snake_case											=							ClapProcessor.from_pretrained(self.tmpdirname       )

         self.assertEqual(processor.tokenizer.get_vocab()     ,   tokenizer.get_vocab()       )
         self.assertIsInstance(processor.tokenizer     ,   __snake_case       )

         self.assertEqual(processor.feature_extractor.to_json_string()     ,   feature_extractor.to_json_string()       )
         self.assertIsInstance(processor.feature_extractor     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							ClapProcessor(tokenizer=self.get_tokenizer()     ,   feature_extractor=self.get_feature_extractor()       )
         processor.save_pretrained(self.tmpdirname       )

         snake_case											=							self.get_tokenizer(bos_token='''(BOS)'''     ,   eos_token='''(EOS)'''       )
         snake_case											=							self.get_feature_extractor(do_normalize=__snake_case     ,   padding_value=1.0       )

         snake_case											=							ClapProcessor.from_pretrained(
             self.tmpdirname     ,   bos_token='''(BOS)'''     ,   eos_token='''(EOS)'''     ,   do_normalize=__snake_case     ,   padding_value=1.0       )

         self.assertEqual(processor.tokenizer.get_vocab()     ,   tokenizer_add_kwargs.get_vocab()       )
         self.assertIsInstance(processor.tokenizer     ,   __snake_case       )

         self.assertEqual(processor.feature_extractor.to_json_string()     ,   feature_extractor_add_kwargs.to_json_string()       )
         self.assertIsInstance(processor.feature_extractor     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.get_feature_extractor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ClapProcessor(tokenizer=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							floats_list((3, 1_0_0_0)       )

         snake_case											=							feature_extractor(__snake_case     ,   return_tensors='''np'''       )
         snake_case											=							processor(audios=__snake_case     ,   return_tensors='''np'''       )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()     ,   input_processor[key].sum()     ,   delta=1E-2       )
    def 						a_     (			self       ):
         snake_case											=							self.get_feature_extractor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ClapProcessor(tokenizer=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							'''This is a test string'''

         snake_case											=							processor(text=__snake_case       )

         snake_case											=							tokenizer(__snake_case       )

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]     ,   encoded_processor[key]       )
    def 						a_     (			self       ):
         snake_case											=							self.get_feature_extractor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ClapProcessor(tokenizer=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         snake_case											=							processor.batch_decode(__snake_case       )
         snake_case											=							tokenizer.batch_decode(__snake_case       )

         self.assertListEqual(__snake_case     ,   __snake_case       )




    def 						a_     (			self       ):
         snake_case											=							self.get_feature_extractor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ClapProcessor(tokenizer=__snake_case     ,   feature_extractor=__snake_case       )

         self.assertListEqual(
             processor.model_input_names[2:]     ,   feature_extractor.model_input_names     ,   msg='''`processor` and `feature_extractor` model input names do not match'''     ,   )




